Exposure minimization response by artificial intelligence

ABSTRACT

An artificial-intelligence system for manipulating resources to minimize exposure harm in a chaotic environment, comprising autonomous agent devices, remote electronic sensors, and a central server. The central server receives a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording values of one or more variables in the chaotic environment; receives a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum permitted risk exposure for the time interval; determines a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment; determines that the weighted total risk exposure exceeds the maximum permitted risk exposure; and causes the autonomous agent devices to manipulate the one or more resources to decrease the weighted total risk exposure.

FIELD OF INVENTION

This application relates to artificial intelligence methods and systems,and more specifically, for methods and systems for an artificialintelligence to analyze sensor data from a chaotic environment andmodify the behavior of an autonomous system in response to changes inthat environment.

BACKGROUND

In many engineering and computing applications, a system must bedesigned with a certain risk tolerance in mind. Buildings are oftendesigned to survive not only a typical average level of wind over a day,but the so called “hundred year storm,” a storm that could possiblyoccur on any given day but is statistically unlikely to occur more oftenthan once per hundred years. Online services for critical commerce orinformation sharing are advertised as having “five nines” availability(i.e., being functional 99.999% of the time, with fewer than six minutesof downtime per year) and need to be able to handle a completelyunpredictable surge in network traffic without dropping incomingrequests for connections.

As a result, there continues to be a need in many computing applicationsand other fields for better anticipation of systemic changes andre-allocation of resources to mitigate the harms from extreme changesthat may or may not imminently occur. This anticipation can befacilitated by the increasing use of distributed sensor systems as partof the “Internet of Things” and the increased incorporation of softwareinto traditionally “dumb” devices to make autonomous vehicles, “smart”thermostats, and other “smart” systems, appliances, and devices thathave a greater awareness of their operating environment and a greatercapability to respond to it.

SUMMARY OF THE INVENTION

Disclosed herein is an artificial-intelligence system for manipulatingresources to minimize exposure harm in a chaotic environment, comprisingone or more autonomous agent devices and a central server. The centralserver comprises a processor and non-transitory memory storinginstructions that, when executed by the processor, cause the processorto: receive a first set of sensor readings from one or more remoteelectronic sensors, during a first time window, the sensor readingsrecording values of one or more variables in the chaotic environment;receive a critical time interval during which the chaotic environmentmay affect one or more of the resources and a maximum permitted riskexposure for the time interval; determine, based on the first set ofsensor readings, a weighted total risk exposure during the critical timeinterval from the chaotic environment and the resources within thechaotic environment; determine that the weighted total risk exposureexceeds the maximum permitted risk exposure; and in response todetermining that that the weighted total risk exposure exceeds themaximum permitted risk exposure, cause the one or more autonomous agentdevices to manipulate the one or more resources to decrease the weightedtotal risk exposure.

Further disclosed is an artificial-intelligence method for manipulatingresources to minimize exposure harm in a chaotic environment,comprising: receiving a first set of sensor readings from one or moreremote electronic sensors, during a first time window, the sensorreadings recording values of one or more variables in the chaoticenvironment; receiving a critical time interval during which the chaoticenvironment may affect one or more of the resources and a maximumpermitted risk exposure for the time interval; determining, based on thefirst set of sensor readings, a weighted total risk exposure during thecritical time interval from the chaotic environment and the resourceswithin the chaotic environment; determining that the weighted total riskexposure exceeds the maximum permitted risk exposure; and in response todetermining that that the weighted total risk exposure exceeds themaximum permitted risk exposure, causing one or more autonomous agentdevices to manipulate the one or more resources to decrease the weightedtotal risk exposure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a computing system for receiving sensor readings from achaotic environment and directing autonomous agents in response tochanges in that chaotic environment;

FIG. 2 depicts a conceptual graph of risk exposure from a hazard over aperiod of time;

FIG. 3 depicts a method for an artificial intelligence system to processthe incoming sensor data and direct the agents in the chaoticenvironment; and

FIG. 4 depicts a general computing device for performing a number offeatures described above.

DETAILED DESCRIPTION

FIG. 1 depicts a computing system for receiving sensor readings from achaotic environment and directing autonomous agents in response tochanges in that chaotic environment.

Many engineering, computing, and social systems are influenced by achaotic environment in which they operate. Buildings, bridges,neighborhoods, and other engineering projects are built in areas thatmay be affected by hurricanes, wildfires, or other environmentalhazards; networked computing devices operate on networks withunpredictable surges in network traffic or rerouting of network trafficdue to broken network links; public utilities and private services mustsupply services to a distributed group of consumers that may demandaccess to service at any time; companies may offer valuable assets fortrade in a market where asset prices are constantly changing and amis-timed offer may be economically wasteful.

The costs of interference from chaotic systems may be realized inincremental or marginal effects (e.g., individual homes being put indanger as a wildfire advances, smoothly decreasing network function astraffic increases, or flickering power access from a strained powergrid, etc.) and/or in sudden, catastrophic losses (e.g., a collapse of amajor bridge from hurricane winds, a server being completely disabled bya denial-of-service attack, or a power grid going completely dark,etc.).

Turning now to the elements of FIG. 1, a central server 100 receives,via a network 110, sensor data from a number of remote electronicsensors 105 that observe or relay data from a chaotic environment inwhich one or more important resources are present. Central server alsotransmits instructions, via network 110, to a number of electroniccomputing device agents 115 that are capable of directly or indirectlyacting to preserve the important resources from harm caused by thechaotic environment.

Network 110 may be, for example, the Internet generally, a localwireless network, an ethernet network or other wired network, asatellite communication system, or any other means of connecting thesensors 105 to the central server 100 and the central server to theagents 115 to enable data transmissions. Moreover, network 110 may notbe a single network, as pictured, rather than a number of separatenetworks; for example, a central server 100 could have a number ofproximal sensors 105 to which it is attached by wired connections, anumber of nearby sensors 105 to which it connects via a Wi-Fi network,and/or a number of extremely remote sensors 105 to which it connects viaa satellite. Connections may avoid the use of a network entirely, anduse direct wired or wireless transmission to send data to and fromcentral server 100. As depicted in FIG. 1, arrows show the expecteddirection of data flow to and from the network.

Sensors 105 may be any types of electronic sensors that register datafrom a chaotic environment external to the central server 100. Examplesensor types for particular embodiments may include, but are not limitedto, cameras, thermometers, GPS tracking devices or other geolocationdevices, sensors of motion/distance/acceleration/orientation of anobject to which the sensor is attached or of a remote object observed bythe sensor, or communications modules receiving electronic datacommunications from a source.

Agents 115 may be any form of computing device (or a module incorporatedinto a device not normally used as a computing device in order tocontrol that device) able to cause the resources to be reallocated,transported, moved, created, destroyed, or otherwise manipulated in away that minimizes a harm from a chaotic environment's interaction withthe resources. For example, an agent 115 could be a computing devicethat controls automated systems within a building, that triggers aphysical alarm, that pilots a drone aircraft or autonomous vehicle, thatroutes network traffic, that generates messages for display on physicaldevices associated with human users, or that performs other actionsassociated with “smart appliances” or other automated systems.

A number of possible pairings of sensors 105 and agents 115 to achieveparticular purposes are described below.

In one example embodiment, listening devices 105 at a power generationplant may receive signals from smart power meters 105 at a number ofhomes and businesses drawing power from the plant. As power drawincreases, the risk of a brownout from insufficient power generation orof blackout from sudden component failure may likewise increase. Anautomated system 115 of the plant may determine whether to spin upadditional turbines, prioritize power output to certain outgoingchannels, or otherwise manipulate power generation resources and thenetwork supplying outgoing power to reduce the risk of a brownout orblackout.

In another example embodiment, a video streaming service may have afirewall or edge network device 105 receiving and routing a number ofrequests to stream certain video files. Each particular customer maybegin watching a movie or episode of half-an-hour or longer that mustcontinue to be supplied smoothly for the duration of that period of timeeven if additional customers begin logging on and requesting other videodata, though some customers will log off before a full movie or episodeis completed. A content delivery network (CDN) management system 115 mayreview the current utilization of the network and distribute copies ofmovie files to secondary CDN servers to minimize the risk that bandwidthwill be completely used and either cause additional customers to be ableto access data, or original customers to experience an interruption inthe viewing experience.

In another example embodiment, an autonomous vehicle with camera,rangefinding, and other sensors 105 may be travelling along a road withsome number of other vehicles, pedestrians, or other objects nearby. Aserver 100 may need to continually assess a number of likelihoods ofcritical accidents, such as a likelihood that a car at a given distanceaway will swerve into the autonomous vehicle's path before theautonomous vehicle is able to avoid it, or that a pedestrian walkingtoward the road will continue walking into the road instead of stoppingat the side, and direct a vehicle control system 115 to change its pathor speed to decrease the risk of an accident.

In another example embodiment, weather satellites, anemometers, orDoppler radar systems 105 may detect weather systems or hazards, such ashurricanes, wildfires, or tornadoes, that may approach neighborhoods,cities, or other settlements. A determination may need to be maderegarding the total cost of an evacuation in response to a hazard,versus the expected cost of not evacuating, which must take into accountthe possibility that the hazard will never reach the human settlements.Similarly, vehicles, ships, or other valuable items may need to be movedin response to an approaching hailstorm, sandstorm, or other weatherevent that may or may not actually affect a given location. Alarm systemor notification system 115 may be configured to trigger if and only ifthe potential harm of not evacuating meets a predefined threshold valueof risk, or autonomous vehicle control systems 115 may be instructed topilot a vehicle to a given location if the risk of damage or destructionof the vehicle becomes unacceptably high based on the current weatherconditions.

In another example embodiment, sensors 105 may include firewalls orrouters at the edge of a computer network, reporting an incoming numberof network packets, while agents 115 may include servers in a servercluster, routers, or firewalls. A system may monitor the current networkutilization and compute the risk that a denial of service attack willoccur and will be able to take down the system under its currentconfiguration. In response, it may either activate more servers tohandle the attack or slow down an inflow of network traffic until therisk of network failure is sufficiently decreased.

In another example embodiment, sensors 105 may include GPS trackers on anumber of animals, or cameras recording the locations of animals in anature preserve. A predator or territorial herbivore in the preserve,such as a lion or elephant, may move somewhat randomly in the vicinityof humans currently in the preserve. A central server may continuallyassess a risk that the animal will encounter the humans before thehumans leave the preserve, and cause notification system, alarm, or thehumans' personal mobile computing devices 115 to warn the humans aboutthe possibility of an encounter and suggest a path that minimizes therisk of the encounter.

In another example embodiment, sensors 105 may include devices at astock exchange or other market reporting the current buy or sell pricesof one or more assets. Agents 115 may include computing devices capableof transmitting buy or sell orders to-or recalling buy or sell ordersfrom-the market, or firewall devices capable of preventing suchcomputing devices from successfully transmitting a buy or sell order tothe market. In response to an exposure to unacceptable levels ofasset-based risk (such as orders to sell an asset at a fixed price or tobuy an asset at whatever price is available in a market where the priceof that asset is rapidly increasing), the ability of traders to trademay be automatically stopped by the computing devices themselves, ortraders may be notified of the anomaly so they may proceed with greatercaution.

In all of the above described systems, there is value in determining atotal risk exposure if no action is taken by the system, and using thisdetermination to decide whether action is warranted to minimize oreliminate the risk to which the system is exposed.

FIGS. 2A and 2B depict conceptual graphs of risk exposure from a hazardover a period of time.

In a simple situation (FIG. 2A) where the expected time until an adverseevent occurs is normally distributed, one might expect to see a graph205 of the probability that the event will occur exactly at time t takethe form of a traditional bell curve. The graph 210 of cumulativeprobability of the event occurring at or before time t would thus have asigmoid shape as it includes the sum of all points on graph 205occurring before it in time.

If it is assumed that the cost of the event from affecting a resource isindependent of when the event occurs, the cumulative risk exposure basedon that resource is simply proportional to the cumulative probability210 at the conclusion of any time window being considered.

If, in contrast, the timing of the event matters in determining the harmof that event, the cumulative risk exposure based on the resource willbe integral of a harm function multiplied by a probabilityfunction-i.e., the sum, for every moment of time considered, of theprobability that the harm will occur at that moment multiplied by theamount of harm that will be incurred if it does.

For example (as illustrated in FIG. 2B), a community may be consideredto incur a harm equal to X if struck by a hurricane while evacuated, butequal to 10X if struck while inhabited. If an evacuation is ordered, theharm function 215 will decrease over time as the people are evacuated,and even though curve 205 of the probability of the event at time t isunchanged, the harm at a given time multiplied by probability of eventat the given time will look very different from the probability curvealone, and so the graph of weighted cumulative exposure risk 220 willbehave differently, with a sharper initial increase and a quickerleveling off. The difference in cumulative exposure risk if evacuationis ordered and a separately computed cumulative exposure risk if noevacuation is ordered may be the basis of an automated system'sdetermination to alert inhabitants or to instruct safety personnel thatan evacuation should begin.

Weighted risk exposures may be calculated for a variety of adverseevents (for example, multiple hurricanes) and/or for a variety ofresources (for example, each settlement that a hurricane might strike)and summed to determine a total weighted risk exposure for an action oran inaction within a given chaotic environment.

FIG. 3 depicts a method for an artificial intelligence system to processthe incoming sensor data and direct the agents in the chaoticenvironment.

First, the system may receive (from an external server, a savedconfiguration file, entry by a human user, or some other source) acritical time window to consider and a maximum permissible weighted riskexposure (Step 300). For example, a weather evacuation system may beconfigured to look out only one week in advance, due to the uncertaintyof data beyond that time window. A system monitoring asset prices may beuninterested in any time period after the market closes for a given day.

Next, the system receives sensor data from sensors 105 regarding theenvironment and resources (Step 305). In some embodiments, the systemmay be preconfigured with information regarding the expected behavior ofelements of the environment or of the resources. In other embodiments,the system may receive sensor data over a period of time and build amodel for the behavior for those elements and resources.

For example, for an element of the chaotic environment that experiencesrandom-walk-like behavior (such as the movements of an animal in apreserve, or the changes in value of an asset in a market), in whichcase the system may determine a standard deviation of volatility forchange in the system, to aid in determining the probability that theelement will change by a given amount during a given interval of time.

In another example, the chaotic environment may experience cyclicalswings, such as increased use of a streaming service or power utility inthe evening and decreased use overnight. The system may determine theperiod of such a cycle and use it in anticipating systemic changesduring future cycles.

After at least an initial amount of sensor data is received andprocessed, the central server determines the total weighted riskexposure of the system (Step 310).

As discussed above, this determination may be made at least in partbased on integrating, for all t before the end of the critical timewindow, P(event occurring at or before t) multiplied by the costfunction for an event at time t.

The system may assume that a given environmental variable (such as theposition of a danger along a given axis or the value of an asset on acertain scale) experiences random-walk-like behavior, and so theprobability of the variable covering a given distance over time is notproportional to the distance, but rather diminishes at a faster ratethan a linear proportion. In a preferred embodiment, the “distance”between two environmental values a and b should not be calculated as|a-b |, but rather as (a-b)². In addition, the sensed values may bemodified before calculation in certain embodiments, such as determininga distance between ln a and ln b rather than between a and b.

Various embodiments may also take into account a natural volatility inthe underlying system whose environmental variables are being sensed.Dividing the distance by a measure of volatility, such as a valueproportional to the standard deviation of the sensed variable valuesover a period of time, will more accurately represent the fact thatrandom walks in faster-paced and more chaotic systems are more likely tocover a distance in a given time interval than those in slower-pacedsystems. The standard deviation may be calculated in terms of a numberof seconds, minutes, or days previous to the current risk exposurecalculation, or a predetermined number of sensor readings regardless ofthe time interval comprising those readings. The standard deviation maybe set to a predetermined default value when either the volume of dataor the brevity of an available previous time window makes the standarddeviation more influenced by noise in the data and less likely toreflect the actual future changes in the variable over a coming timewindow.

In a preferred embodiment including a squared distance term and anallowance for volatility as described above, if the random-walk-likebehavior has a standard deviation of σ with a current mean value of z₀,the cumulative probability of the environmental variable reaching acertain value z at exactly time t is equal to

$\frac{\left( {z - z_{0}} \right)}{\sqrt{2\pi \sigma^{2}t^{3}}}e^{\frac{- {({z - z_{0}})}^{2}}{2\pi \sigma^{2}t}}$

Given this assumption, the integral of this function over a totalremaining time interval of length T (i.e., the cumulative probabilitythat the event will occur at some point during the interval) is

${ERFC}\mspace{14mu} \left( \frac{{z - z_{0}}}{\sigma \sqrt{2T}} \right)$

where ERFC is the complementary error function. If the cost function ofan event is constant with respect to time, the total weighted riskexposure will thus be equal to

$\sum\limits_{k}\left( {C_{k} \times {{ERFC}\ \left( \frac{{z_{k} - z_{0_{k}}}}{\sigma_{k}\sqrt{2T}} \right)}} \right)$

where k represents each resource and C_(k) the cost to that resource ifthe event occurs.

Other probability functions may be used to estimate probabilities ofchange in systems that do not seem to experience random-walk behavior.These probability functions may likewise be integrated with respect totime to determine a shortcut calculation for a cumulative probability ofan event and for determining an overall weighted risk exposure.

The cost associated with a given resource may be an estimate, forexample, of the cost of repairing a physical item, the abstracted costof pain, suffering, or loss of life, abstracted costs of loss ofgoodwill for a consumer-facing system going down when consumers arerelying on it, or pure economic losses from participation in anill-advised trade in a market setting. In many cases, the cost may be anexpected cost for a class of event that itself may have a wide range ofpossible actual costs.

After determining total weighted risk exposure, the system determineswhether a total weighted risk exposure exceeds the stored maximumpermissible weighted exposure (Step 315).

If there is not excessive risk exposure, the system returns to receivingsensor data during a new period of time (Step 305) and recalculatingwhether the risk exposure has changed in response to updated data fromthe sensors 105. In some embodiments, the total number of calculationsinvolved in recalculating total risk exposure may be onerous, and thesystem may only recalculate the total weighted risk exposure when agiven environmental variable has changed by at least a certain minimumthreshold amount from the last time that the total weighted riskexposure was calculated. Additionally, in some embodiments, the systemmay round, truncate, or otherwise preprocess environmental variable databefore recalculation in order to simply recalculation or determine thatit is not yet necessary.

If, on the other hand, there is excessive risk exposure, the system maytransmit a message or instruction to agents 115 (Step 320) in order tobegin minimizing risk through the moving resources, notifying humanusers of the risk to the resources, or otherwise reconfiguring resourcesto minimize the risk from the environment, as described in embodimentsabove. After transmission of the message, the system returns toobserving sensor data (Step 305 and following) and determining whetheran excessive risk exposure still exists and requires further action bythe agents.

In a specific embodiment directed to minimizing exposure within a stockmarket setting, additional considerations and actions may be taken inthe determining of the exposure or the response to it.

For example, in addition to the changes in an asset price that an orderrefers to, the order itself may be modified, such as changing an offeredprice, an offered volume, a status (offered, accepted, or filled). An“order book” containing all open orders may be maintained and updated asorders are offered, modified, or filled, affecting the total exposure anentity may have across one or more stock exchanges. The softwaremaintaining an order book may need to keep the order book accuratedespite complex chains of reported status updates and modifications,such as an order which is offered, modified, accepted, modified again,and filled partially from one source and partially from another. Thetask may be further complicated by messages being received out of order,such as confirmation of an order modification being received beforenotification of the request to modify the order, so that incomingmessages may be queued until the context needed to make sense of them isalso received.

The total order book for a firm may be used to estimate the totalfinancial risk for the firm at a given moment in time-for example, ifthe firm has outstanding offers to buy an asset at a given price, butthe market price has decreased below that given price and allowed othertraders to arbitrage and essentially receive free money at the firm'sexpense. Using the mathematical calculations described above,outstanding offers may be weighted by a probability that they willactually be filled, which will be inversely related to the distance ofan offer's current price and the market's current price. As the marketprice fluctuates or the offer price is modified, the total risk exposurefor a firm may be recalculated to determine whether action is necessaryto reduce that exposure. Examples of actions may include automaticallygenerating a modification instruction for one or more outstanding ordersand transmitting the instruction to one or more exchanges on which thoseorders have been placed; automatically generating a notice for a humanuser that an increased level of financial risk has been reached andadditional care should be exercised; or even preventing (via controlover a communications interface of a computer used for trading) thetransmission of additional orders to the exchanges until total riskexposure has decreased or a human user has authorized the resumption oftrading.

FIG. 4 is a high-level block diagram of a representative computingdevice that may be utilized to implement various features and processesdescribed herein, for example, the functionality of central server 100,sensors 105, or autonomous agents 115. The computing device may bedescribed in the general context of computer system-executableinstructions, such as program modules, being executed by a computersystem. Generally, program modules may include routines, programs,objects, components, logic, data structures, and so on that performparticular tasks or implement particular abstract data types.

As shown in FIG. 4, the computing device is illustrated in the form of aspecial purpose computer system. The components of the computing devicemay include (but are not limited to) one or more processors orprocessing units 900, a system memory 910, and a bus 915 that couplesvarious system components including memory 910 to processor 900.

Bus 915 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Processing unit(s) 900 may execute computer programs stored in memory910. Any suitable programming language can be used to implement theroutines of particular embodiments including C, C++, Java, assemblylanguage, etc. Different programming techniques can be employed such asprocedural or object oriented. The routines can execute on a singlecomputing device or multiple computing devices. Further, multipleprocessors 900 may be used.

The computing device typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby the computing device, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 910 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 920 and/or cachememory 930. The computing device may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 940 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically referred to as a “hard drive”). Although notshown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to bus915 by one or more data media interfaces. As will be further depictedand described below, memory 910 may include at least one program producthaving a set (e.g., at least one) of program modules that are configuredto carry out the functions of embodiments described in this disclosure.

Program/utility 950, having a set (at least one) of program modules 955,may be stored in memory 910 by way of example, and not limitation, aswell as an operating system, one or more application software, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment.

The computing device may also communicate with one or more externaldevices 970 such as a keyboard, a pointing device, a display, etc.; oneor more devices that enable a user to interact with the computingdevice; and/or any devices (e.g., network card, modem, etc.) that enablethe computing device to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O)interface(s) 960.

In addition, as described above, the computing device can communicatewith one or more networks, such as a local area network (LAN), a generalwide area network (WAN) and/or a public network (e.g., the Internet) vianetwork adaptor 980. As depicted, network adaptor 980 communicates withother components of the computing device via bus 915. It should beunderstood that although not shown, other hardware and/or softwarecomponents could be used in conjunction with the computing device.Examples include (but are not limited to) microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed:
 1. An artificial-intelligence system for manipulatingresources to minimize exposure harm in a chaotic environment,comprising: one or more autonomous agent devices; and a central servercomprising a processor and non-transitory memory storing instructionsthat, when executed by the processor, cause the processor to: receive afirst set of sensor readings from one or more remote electronic sensors,during a first time window, the sensor readings recording values of oneor more variables in the chaotic environment; receive a critical timeinterval during which the chaotic environment may affect one or more ofthe resources and a maximum permitted risk exposure for the timeinterval; determine, based on the first set of sensor readings, aweighted total risk exposure during the critical time interval from thechaotic environment and the resources within the chaotic environment;determine that the weighted total risk exposure exceeds the maximumpermitted risk exposure; and in response to determining that that theweighted total risk exposure exceeds the maximum permitted riskexposure, cause the one or more autonomous agent devices to manipulatethe one or more resources to decrease the weighted total risk exposure.2. The system of claim 1, wherein the non-transitory memory storesinstructions that, when executed by the processor, further cause theprocessor to: receive a second set of sensor readings from the one ormore remote electronic sensors during the second time window recordingchange in the one or more variables; and update the weighted total riskexposure during the critical time interval based at least in part on thesecond set of sensor readings.
 3. The system of claim 2, wherein thenon-transitory memory stores instructions that, when executed by theprocessor, further cause the processor to: subsequent to manipulatingthe one or more resources to decrease the weighted total risk exposureand to updating the weighted total risk exposure, repeatedlymanipulating the one or more resources and updating the weighted totalrisk exposure until the weighted total risk exposure does not exceed themaximum permitted risk exposure.
 4. The system of claim 1, wherein thedetermination of the weighted total risk exposure is based at least inpart on integration of a risk function with respect to time from apresent moment to the conclusion of the critical time interval.
 5. Thesystem of claim 4, wherein the risk function is based on an assumptionof random walk behavior in the one or more variables.
 6. The system ofclaim 5, wherein the risk function is based at least in part on thecomplementary error function.
 7. The system of claim 1, wherein the oneor more autonomous agent devices manipulate the one or more resources bypreventing a network message from being transmitted through a network.8. The system of claim 1, wherein the one or more autonomous agentdevices manipulate the one or more resources by transmitting a networkmessage to a remote computing device.
 9. The system of claim 1, whereinthe one or more autonomous agent devices manipulate the one or moreresources by generating a message for receipt by a human user.
 10. Thesystem of claim 1, wherein the one or more autonomous agent devicesmanipulate the one or more resources by activating an alarm visible oraudible to a human user.
 11. An artificial-intelligence method formanipulating resources to minimize exposure harm in a chaoticenvironment, comprising: receiving a first set of sensor readings fromone or more remote electronic sensors, during a first time window, thesensor readings recording values of one or more variables in the chaoticenvironment; receiving a critical time interval during which the chaoticenvironment may affect one or more of the resources and a maximumpermitted risk exposure for the time interval; determining, based on thefirst set of sensor readings, a weighted total risk exposure during thecritical time interval from the chaotic environment and the resourceswithin the chaotic environment; determining that the weighted total riskexposure exceeds the maximum permitted risk exposure; and in response todetermining that that the weighted total risk exposure exceeds themaximum permitted risk exposure, causing one or more autonomous agentdevices to manipulate the one or more resources to decrease the weightedtotal risk exposure.
 12. The method of claim 11, wherein thenon-transitory memory stores instructions that, when executed by theprocessor, further cause the processor to: receive a second set ofsensor readings from the one or more remote electronic sensors duringthe second time window recording change in the one or more variables;and update the weighted total risk exposure during the critical timeinterval based at least in part on the second set of sensor readings.13. The method of claim 12, wherein the non-transitory memory storesinstructions that, when executed by the processor, further cause theprocessor to: subsequent to manipulating the one or more resources todecrease the weighted total risk exposure and to updating the weightedtotal risk exposure, repeatedly manipulating the one or more resourcesand updating the weighted total risk exposure until the weighted totalrisk exposure does not exceed the maximum permitted risk exposure. 14.The method of claim 11, wherein the determination of the weighted totalrisk exposure is based at least in part on integration of a riskfunction with respect to time from a present moment to the conclusion ofthe critical time interval.
 15. The method of claim 14, wherein the riskfunction is based on an assumption of random walk behavior in the one ormore variables.
 16. The method of claim 15, wherein the risk function isbased at least in part on the complementary error function.
 17. Themethod of claim 11, wherein the one or more autonomous agent devicesmanipulate the one or more resources by preventing a network messagefrom being transmitted through a network.
 18. The method of claim 11,wherein the one or more autonomous agent devices manipulate the one ormore resources by transmitting a network message to a remote computingdevice.
 19. The method of claim 11, wherein the one or more autonomousagent devices manipulate the one or more resources by generating amessage for receipt by a human user.
 20. The method of claim 11, whereinthe one or more autonomous agent devices manipulate the one or moreresources by activating an alarm visible or audible to a human user.